Evidence map›Paper›PMID 39000719›Full record

ReviewPolymers2024

Molecular Dynamic Simulations for Biopolymers with Biomedical Applications.

Ramón Garduño-Juárez, David O Tovar-Anaya, Jose Manuel Perez-Aguilar, Luis Fernando Lozano-Aguirre Beltran, Rafael A Zubillaga, Marco Antonio Alvarez-Perez, Eduardo Villarreal-Ramirez

Abstract readReview
In one paragraph

Review in Polymers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Dynamical networking of polymer networks with dedicated cross-linker particles.The European physical journal. E, Soft matter · 2026
    Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Dynamical networking using Gaussian fields.The European physical journal. E, Soft matter · 2025
    Article
  11. Review
  12. Review
  13. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Ramón Garduño-JuárezInstituto de Ciencias Físicas, Universidad Nacional Autónoma de México, Cuernavaca 62210, Mexico.ORCID 0000-0002-4407-9828
David O Tovar-AnayaLaboratorio de Bioingeniería de Tejidos, División de Estudios de Posgrado e Investigación, Coyoacán 04510, Mexico.ORCID 0009-0005-5762-0504
Jose Manuel Perez-AguilarSchool of Chemical Sciences, Meritorious Autonomous University of Puebla (BUAP), University City, Puebla 72570, Mexico.ORCID 0000-0002-9893-5992
Luis Fernando Lozano-Aguirre BeltranCentro de Ciencias Genómicas, Universidad Nacional Autónoma de Mexico, Cuernavaca 62210, Mexico.
Rafael A ZubillagaDepartamento de Química, Universidad Autónoma Metropolitana-Iztapalapa, Mexico City 09340, Mexico.ORCID 0000-0003-1919-5015
Marco Antonio Alvarez-PerezLaboratorio de Bioingeniería de Tejidos, División de Estudios de Posgrado e Investigación, Coyoacán 04510, Mexico.
Eduardo Villarreal-RamirezLaboratorio de Bioingeniería de Tejidos, División de Estudios de Posgrado e Investigación, Coyoacán 04510, Mexico.ORCID 0000-0003-0879-8769

Funding

CONAHCYT A1-S-9178Universidad Nacional Autónoma de México DGAPA-UNAM-PAPIIT IN230220 & IN209123 - LANCAD-UNAM-DGTIC-324
6 · The paper itself

Abstract

Computational modeling (CM) is a versatile scientific methodology used to examine the properties and behavior of complex systems, such as polymeric materials for biomedical bioengineering. CM has emerged as a primary tool for predicting, setting up, and interpreting experimental results. Integrating in silico and in vitro experiments accelerates scientific advancements, yielding quicker results at a reduced cost. While CM is a mature discipline, its use in biomedical engineering for biopolymer materials has only recently gained prominence. In biopolymer biomedical engineering, CM focuses on three key research areas: (A) Computer-aided design (CAD/CAM) utilizes specialized software to design and model biopolymers for various biomedical applications. This technology allows researchers to create precise three-dimensional models of biopolymers, taking into account their chemical, structural, and functional properties. These models can be used to enhance the structure of biopolymers and improve their effectiveness in specific medical applications. (B) Finite element analysis, a computational technique used to analyze and solve problems in engineering and physics. This approach divides the physical domain into small finite elements with simple geometric shapes. This computational technique enables the study and understanding of the mechanical and structural behavior of biopolymers in biomedical environments. (C) Molecular dynamics (MD) simulations involve using advanced computational techniques to study the behavior of biopolymers at the molecular and atomic levels. These simulations are fundamental for better understanding biological processes at the molecular level. Studying the wide-ranging uses of MD simulations in biopolymers involves examining the structural, functional, and evolutionary aspects of biomolecular systems over time. MD simulations solve Newton's equations of motion for all-atom systems, producing spatial trajectories for each atom. This provides valuable insights into properties such as water absorption on biopolymer surfaces and interactions with solid surfaces, which are crucial for assessing biomaterials. This review provides a comprehensive overview of the various applications of MD simulations in biopolymers. Additionally, it highlights the flexibility, robustness, and synergistic relationship between in silico and experimental techniques.

Indexed as

biopolymerbiopolymersmolecular dynamics simulationspolymeric materialsprotein absorption

Identifiers

PMID39000719
PMCPMC11244511

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.